{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "首先介绍一幅Matplotlib图形的各个组成部分。  \n",
    "\n",
    "在matplotlib中，整个图像为一个Figure对象。在Figure对象中可以包含一个或多个Axes对象，每个Axes(ax)对象都是一个拥有自己坐标系统的绘图区域。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "subplots(nrows=1, ncols=1, sharex=False, sharey=False, squeeze=True, subplot_kw=None, gridspec_kw=None, **fig_kw)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**参数：**   \n",
    "nrows, ncols：\n",
    "- 子图的行列数  \n",
    "\n",
    "sharex, sharey：  \n",
    "- 设置为True或all时，所有子图共享x轴或y轴；  \n",
    "- 设置为False或none时，所有子图的x, y 轴均为独立；  \n",
    "- 设置为'row'时，每一行的子图会共享x或y轴；  \n",
    "- 设置为'col'时，每一列的子图会共享x或y轴。  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function subplots in module matplotlib.pyplot:\n",
      "\n",
      "subplots(nrows=1, ncols=1, sharex=False, sharey=False, squeeze=True, subplot_kw=None, gridspec_kw=None, **fig_kw)\n",
      "    Create a figure and a set of subplots\n",
      "    \n",
      "    This utility wrapper makes it convenient to create common layouts of\n",
      "    subplots, including the enclosing figure object, in a single call.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    nrows, ncols : int, optional, default: 1\n",
      "        Number of rows/columns of the subplot grid.\n",
      "    \n",
      "    sharex, sharey : bool or {'none', 'all', 'row', 'col'}, default: False\n",
      "        Controls sharing of properties among x (`sharex`) or y (`sharey`)\n",
      "        axes:\n",
      "    \n",
      "            - True or 'all': x- or y-axis will be shared among all\n",
      "              subplots.\n",
      "            - False or 'none': each subplot x- or y-axis will be\n",
      "              independent.\n",
      "            - 'row': each subplot row will share an x- or y-axis.\n",
      "            - 'col': each subplot column will share an x- or y-axis.\n",
      "    \n",
      "        When subplots have a shared x-axis along a column, only the x tick\n",
      "        labels of the bottom subplot are visible.  Similarly, when subplots\n",
      "        have a shared y-axis along a row, only the y tick labels of the first\n",
      "        column subplot are visible.\n",
      "    \n",
      "    squeeze : bool, optional, default: True\n",
      "        - If True, extra dimensions are squeezed out from the returned Axes\n",
      "          object:\n",
      "    \n",
      "            - if only one subplot is constructed (nrows=ncols=1), the\n",
      "              resulting single Axes object is returned as a scalar.\n",
      "            - for Nx1 or 1xN subplots, the returned object is a 1D numpy\n",
      "              object array of Axes objects are returned as numpy 1D arrays.\n",
      "            - for NxM, subplots with N>1 and M>1 are returned as a 2D arrays.\n",
      "    \n",
      "        - If False, no squeezing at all is done: the returned Axes object is\n",
      "          always a 2D array containing Axes instances, even if it ends up\n",
      "          being 1x1.\n",
      "    \n",
      "    subplot_kw : dict, optional\n",
      "        Dict with keywords passed to the\n",
      "        :meth:`~matplotlib.figure.Figure.add_subplot` call used to create each\n",
      "        subplot.\n",
      "    \n",
      "    gridspec_kw : dict, optional\n",
      "        Dict with keywords passed to the\n",
      "        :class:`~matplotlib.gridspec.GridSpec` constructor used to create the\n",
      "        grid the subplots are placed on.\n",
      "    \n",
      "    **fig_kw :\n",
      "        All additional keyword arguments are passed to the :func:`figure` call.\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    fig : :class:`matplotlib.figure.Figure` object\n",
      "    \n",
      "    ax : Axes object or array of Axes objects.\n",
      "    \n",
      "        ax can be either a single :class:`matplotlib.axes.Axes` object or an\n",
      "        array of Axes objects if more than one subplot was created.  The\n",
      "        dimensions of the resulting array can be controlled with the squeeze\n",
      "        keyword, see above.\n",
      "    \n",
      "    Examples\n",
      "    --------\n",
      "    First create some toy data:\n",
      "    \n",
      "    >>> x = np.linspace(0, 2*np.pi, 400)\n",
      "    >>> y = np.sin(x**2)\n",
      "    \n",
      "    Creates just a figure and only one subplot\n",
      "    \n",
      "    >>> fig, ax = plt.subplots()\n",
      "    >>> ax.plot(x, y)\n",
      "    >>> ax.set_title('Simple plot')\n",
      "    \n",
      "    Creates two subplots and unpacks the output array immediately\n",
      "    \n",
      "    >>> f, (ax1, ax2) = plt.subplots(1, 2, sharey=True)\n",
      "    >>> ax1.plot(x, y)\n",
      "    >>> ax1.set_title('Sharing Y axis')\n",
      "    >>> ax2.scatter(x, y)\n",
      "    \n",
      "    Creates four polar axes, and accesses them through the returned array\n",
      "    \n",
      "    >>> fig, axes = plt.subplots(2, 2, subplot_kw=dict(polar=True))\n",
      "    >>> axes[0, 0].plot(x, y)\n",
      "    >>> axes[1, 1].scatter(x, y)\n",
      "    \n",
      "    Share a X axis with each column of subplots\n",
      "    \n",
      "    >>> plt.subplots(2, 2, sharex='col')\n",
      "    \n",
      "    Share a Y axis with each row of subplots\n",
      "    \n",
      "    >>> plt.subplots(2, 2, sharey='row')\n",
      "    \n",
      "    Share both X and Y axes with all subplots\n",
      "    \n",
      "    >>> plt.subplots(2, 2, sharex='all', sharey='all')\n",
      "    \n",
      "    Note that this is the same as\n",
      "    \n",
      "    >>> plt.subplots(2, 2, sharex=True, sharey=True)\n",
      "    \n",
      "    See Also\n",
      "    --------\n",
      "    figure\n",
      "    subplot\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "help(plt.subplots)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x9bbe828>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# First create some toy data\n",
    "x = np.linspace(0, 2*np.pi, 400)\n",
    "y = np.sin(x**2)\n",
    "\n",
    "# create just a figure and only one subplot\n",
    "fig, ax = plt.subplots()\n",
    "ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x63ccbe0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create two subplots and unpacks the output array immediately\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, sharey=True)\n",
    "ax1.plot(x, y)\n",
    "ax1.set_title('Sharing Y axis')\n",
    "ax2.scatter(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x9e9f400>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create four polar axes, and accesses them through the returned array\n",
    "fig, axes = plt.subplots(2, 2, subplot_kw=dict(polar=True)) # \n",
    "axes[0,0].plot(x,y)\n",
    "axes[1,1].scatter(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 432x288 with 4 Axes>,\n",
       " array([[<matplotlib.axes._subplots.AxesSubplot object at 0x00000000095383C8>,\n",
       "         <matplotlib.axes._subplots.AxesSubplot object at 0x0000000009563A90>],\n",
       "        [<matplotlib.axes._subplots.AxesSubplot object at 0x00000000095940F0>,\n",
       "         <matplotlib.axes._subplots.AxesSubplot object at 0x00000000095BA630>]],\n",
       "       dtype=object))"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# share a X axis with each column of subplots\n",
    "plt.subplots(2, 2, sharex='col')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 432x288 with 4 Axes>,\n",
       " array([[<matplotlib.axes._subplots.AxesSubplot object at 0x0000000009659BA8>,\n",
       "         <matplotlib.axes._subplots.AxesSubplot object at 0x000000000967E438>],\n",
       "        [<matplotlib.axes._subplots.AxesSubplot object at 0x00000000096A4978>,\n",
       "         <matplotlib.axes._subplots.AxesSubplot object at 0x00000000096CFFD0>]],\n",
       "       dtype=object))"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# share a Y axis with each row of subplots\n",
    "plt.subplots(2, 2, sharey='row')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 432x288 with 6 Axes>,\n",
       " array([[<matplotlib.axes._subplots.AxesSubplot object at 0x0000000009936CC0>,\n",
       "         <matplotlib.axes._subplots.AxesSubplot object at 0x000000000996C0F0>,\n",
       "         <matplotlib.axes._subplots.AxesSubplot object at 0x00000000099954E0>],\n",
       "        [<matplotlib.axes._subplots.AxesSubplot object at 0x00000000099BE898>,\n",
       "         <matplotlib.axes._subplots.AxesSubplot object at 0x00000000099E7CC0>,\n",
       "         <matplotlib.axes._subplots.AxesSubplot object at 0x0000000009A190B8>]],\n",
       "       dtype=object))"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# share both X and Y axis with all of subplots\n",
    "plt.subplots(2, 3, sharex='all',sharey='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
